#replace ND with 0
tr <- matrix(data = NA, ncol = ncol(dt[,c(1:46)]), nrow=nrow(dt))
colnames(tr) <- colnames(dt[,c(1:46)])
for (i in 12:46)
{
tr[,c(i)] <- gsub(".*ND.*", 0, dt[,i])
}
for(i in 1:11)
{
tr[,c(i)] <- dt[,c(i)]
}
#transform to dataframe
tr <- as.data.frame.matrix(tr) #A correct command to change the dataset to dataframe after transformations
tr[,12:46] <- sapply(tr[,12:46],as.numeric) # Change a character to numeric (double)
typeof(tr$Cu_concentration) # confirm the value is no longer a character## [1] "double"
head(tr)| Scientific_Name | Group | Plot | Sample_Name | Tube_No | Type_of_Sample | Total_Weight | Cup_No | pXRF_measurement_ID | File | Material | Cl_concentration | Cl_uncertainty | Ca_concentration | Ca_uncertainty | Ti_concentration | Ti_uncertainty | Cr_concentration | Cr_uncertainty | Mn_concentration | Mn_uncertainty | Fe_concentration | Fe_uncertainty | Co_concentration | Co_uncertainty | Ni_concentration | Ni_uncertainty | Cu_concentration | Cu_uncertainty | Zn_concentration | Zn_uncertainty | As_concentration | As_uncertainty | Se_concentration | Se_uncertainty | Cd_concentration | Cd_uncertainty | Re_concentration | Re_uncertainty | Hg_concentration | Hg_uncertainty | Tl_concentration | Tl_uncertainty | Pb_concentration | Pb_uncertainty | Substrate_RT |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Allionia incarnata | G3 | P1 | P1_29_1 | 30;31 | leaf | 0.55 | 17 | 2126 | Scan2126_19.14.hdx | Plant | 580 | 268 | 48102 | 704 | 143.0 | 27.5 | 7.7 | 5.5 | 36.2 | 10.0 | 1037 | 33.0 | 0 | 2.5 | 0 | 1.2 | 37.5 | 4.5 | 0.0 | 1.1 | 5.0 | 0.3 | 1.0 | 0.6 | 0.0 | 2.5 | 0.0 | 1.5 | 0 | 0.4 | 0 | 0.6 | 0 | 0.9 | 0.0575852 |
| Allionia incarnata | G3 | P1 | P1_30 | 28 | leaf | 0.166 | 18 | 2127 | Scan2127_19.19.hdx | Plant | 306 | 178 | 22621 | 439 | 132.0 | 22.5 | 4.8 | 4.0 | 47.8 | 10.7 | 1709 | 46.9 | 0 | 3.3 | 0 | 1.2 | 8.1 | 3.2 | 11.8 | 3.8 | 2.5 | 1.5 | 0.0 | 0.6 | 0.0 | 4.7 | 0.0 | 2.1 | 0 | 0.6 | 0 | 0.7 | 0 | 2.5 | 0.0143636 |
| Allionia incarnata | G3 | P1 | P1_29_1_2 | 29 | leaf | 0.213 | 19 | 2128 | Scan2128_19.25.hdx | Plant | 527 | 259 | 47147 | 698 | 124.0 | 25.7 | 10.9 | 6.2 | 25.2 | 9.1 | 866 | 30.8 | 0 | 2.3 | 0 | 1.2 | 35.9 | 5.0 | 0.0 | 1.2 | 6.5 | 0.4 | 0.8 | 0.6 | 0.0 | 3.8 | 0.0 | 1.9 | 0 | 0.5 | 0 | 0.5 | 0 | 1.0 | 0.0345624 |
| Allionia incarnata | G3 | P2 | P2_E12 | 33;34 | leaf | 0.332 | 20 | 2129 | Scan2129_19.30.hdx | Plant | 2576 | 462 | 37856 | 611 | 146.0 | 26.3 | 10.9 | 6.0 | 30.7 | 10.0 | 1320 | 35.8 | 0 | 2.5 | 0 | 1.2 | 47.1 | 5.1 | 0.0 | 1.8 | 6.2 | 0.4 | 2.6 | 0.7 | 0.0 | 4.5 | 4.6 | 2.6 | 0 | 0.5 | 0 | 0.5 | 0 | 0.8 | 0.0542008 |
| Allionia incarnata | G3 | P2 | P2_E12_1 | 32 | leaf | 0.183 | 21 | 2130 | Scan2130_19.35.hdx | Plant | 4756 | 619 | 29095 | 530 | 90.3 | 20.5 | 10.6 | 5.6 | 0.0 | 5.8 | 668 | 26.6 | 0 | 2.0 | 0 | 1.2 | 26.1 | 4.6 | 0.0 | 1.5 | 4.4 | 1.2 | 2.7 | 1.0 | 20.1 | 20.1 | 6.4 | 3.4 | 0 | 0.7 | 0 | 0.9 | 0 | 1.9 | 0.0248243 |
| Allionia incarnata | G3 | P2 | P2_E12_2 | 26 | leaf | 0.164 | 22 | 2131 | Scan2131_19.39.hdx | Plant | 753 | 234 | 6209 | 233 | 30.6 | 11.5 | 0.0 | 2.5 | 0.0 | 5.1 | 371 | 23.4 | 0 | 2.2 | 0 | 1.5 | 7.9 | 3.2 | 0.0 | 1.7 | 3.3 | 1.6 | 3.6 | 1.5 | 0.0 | 14.2 | 0.0 | 2.9 | 0 | 0.8 | 0 | 1.2 | 0 | 2.8 | 0.0110434 |
#Filtering with tydeverse library
dt_plants <- filter(tr, Scientific_Name != 'QA_Sample')
P1 <- filter(dt_plants, Plot == "P1")
P2 <- filter(dt_plants, Plot == "P2")
P5 <- filter(dt_plants, Plot == "P5")
P6 <- filter(dt_plants, Plot == "P6")
P125 <- filter(dt_plants, Plot != "P6")
Se_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Populus fremontii' | Scientific_Name == 'Senegalia (Acacia) greggii' )
Re_best <- subset(dt_plants, Scientific_Name == 'Isocoma cf. tenuisecta' | Scientific_Name == 'Baccharis sarothroides' | Scientific_Name == 'Senegalia (Acacia) greggii'| Scientific_Name == 'Nultuma (Prosopis) velutina' | Scientific_Name == 'Mimosa biuncifera (=aculeaticarpa)' | Scientific_Name == 'Fraxinus velutina'| Scientific_Name == 'Datura wrightii' )
# Dropping uncertainty columns for PCA analysis
dt_plants_nounc = select(dt_plants, -Cl_uncertainty,-Ca_uncertainty, -Ti_uncertainty,
-Cr_uncertainty, -Mn_uncertainty, -Fe_uncertainty, -Ni_uncertainty, -Cu_uncertainty,
-Zn_uncertainty, -As_uncertainty, -Se_uncertainty, -Cd_uncertainty, -Re_uncertainty, -Hg_uncertainty, -Co_uncertainty,
-Tl_uncertainty, -Pb_uncertainty, -Substrate_RT)
dt_plants_nounc = select(dt_plants_nounc, -Hg_concentration, -Tl_concentration, -Pb_concentration, -Ni_concentration, -Co_concentration)
#Filtering plants By Plot with subset function
dt_plants_nounc1 <- subset(dt_plants_nounc, Plot=="P1")
dt_plants_nounc2 <- subset(dt_plants_nounc, Plot=="P2")
dt_plants_nounc5 <- subset(dt_plants_nounc, Plot=="P5")
dt_plants_nounc6 <- subset(dt_plants_nounc, Plot=="P6")
dt_plants_nounce15 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5")
dt_plants_nounce125 <- subset(dt_plants_nounc, Plot=="P1" | Plot=="P5" | Plot=="P2")
#Removing _concentration from column names
colnames(dt_plants_nounce125)[12] <- "Cl"
colnames(dt_plants_nounce125)[13] <- "Ca"
colnames(dt_plants_nounce125)[14] <- "Ti"
colnames(dt_plants_nounce125)[15] <- "Cr"
colnames(dt_plants_nounce125)[16] <- "Mn"
colnames(dt_plants_nounce125)[17] <- "Fe"
colnames(dt_plants_nounce125)[18] <- "Cu"
colnames(dt_plants_nounce125)[19] <- "Zn"
colnames(dt_plants_nounce125)[20] <- "As"
colnames(dt_plants_nounce125)[21] <- "Se"
colnames(dt_plants_nounce125)[22] <- "Cd"
colnames(dt_plants_nounce125)[23] <- "Re"
colnames(dt_plants_nounc6)[12] <- "Cl"
colnames(dt_plants_nounc6)[13] <- "Ca"
colnames(dt_plants_nounc6)[14] <- "Ti"
colnames(dt_plants_nounc6)[15] <- "Cr"
colnames(dt_plants_nounc6)[16] <- "Mn"
colnames(dt_plants_nounc6)[17] <- "Fe"
colnames(dt_plants_nounc6)[18] <- "Cu"
colnames(dt_plants_nounc6)[19] <- "Zn"
colnames(dt_plants_nounc6)[20] <- "As"
colnames(dt_plants_nounc6)[21] <- "Se"
colnames(dt_plants_nounc6)[22] <- "Cd"
colnames(dt_plants_nounc6)[23] <- "Re"Cu_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Cu_AllPlotsRe_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
#ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Re_AllPlotsRe_box <- ggplot(Re_best, aes(x = reorder(Scientific_Name, Re_concentration, FUN = median), y = Re_concentration, fill=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 45))+
geom_jitter(color="#85b8bc", size=2, alpha=0.9) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
#scale_fill_manual(values = c("", "", "", "", "", "","" ))
scale_fill_manual(values = c("#4b2866", "#c7abdd", "#a578c9", "#381e4c", "#8347b2", "#5d327f","#251433" ))
#scale_fill_brewer(palette = "Greens")
Re_boxZn_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Zn_concentration, FUN = median), y = Zn_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
#ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Zn_AllPlotsSe_AllPlots<- ggplot(dt_plants, aes(x = reorder(Scientific_Name, Se_concentration, FUN = median), y = Se_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1) +
ylim(0,60)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E"))
Se_AllPlotsSe_box <- ggplot(Se_best, aes(x = reorder(Scientific_Name, Se_concentration, FUN=median), y = Se_concentration, fill=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 45))+
geom_jitter(color="#85b8bc", size=3, alpha=0.9) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values = c("#251433", "#c7abdd", "#8347b2"))
Se_boxPlants collected at the plot 6 were growing directly on the mine tailings that were exposed on the area of 100 x 100 m. Shrubs were also collected in the close vicinity to the tailings given their rooting depths.
Plot 6
Cu_Plot6 <- ggplot(P6, aes(x = reorder(Scientific_Name, Cu_concentration, FUN = median), y = Cu_concentration, group=Scientific_Name)) +
geom_boxplot()+theme_classic()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),axis.title.x=element_blank())+
#theme(legend.position = "none")+
scale_x_discrete(guide = guide_axis(angle = 0))+
geom_jitter(aes(colour = Plot), size=1.6) +
ylim(0,600)+
coord_flip()+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#scale_fill_manual(values = c("#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08", "#CC503E", "#38A6A5", "#73AF48", "#EDAD08"))
Cu_Plot6require(stats)
myPr1 <- prcomp(dt_plants_nounc1[,12:23], scale=TRUE)
myPr2 <- prcomp(dt_plants_nounc2[,12:23], scale=TRUE)
myPr5 <- prcomp(dt_plants_nounc5[,12:23], scale=TRUE)
myPr6 <- prcomp(dt_plants_nounc6[,12:23], scale=TRUE)
myPr15 <- prcomp(dt_plants_nounce15[,12:23], scale=TRUE)
myPr125 <- prcomp(dt_plants_nounce125[,12:23], scale=TRUE) # it was not working because the scale was FALSEbiplot(myPr1, scale=0)biplot(myPr125, scale=0)biplot125 <- biplot(myPr125,
col=c('blue', 'red'),
cex=c(0.8, 0.8),
xlim=c(-.4, .4),
main='PCA Results',
expand=1.2)biplot6 <- biplot(myPr6,
col=c('blue', 'red'),
cex=c(0.8, 0.8),
xlim=c(-.4, .4),
main='PCA Results',
expand=1.2)dt_plants1 <- cbind(dt_plants_nounc1, myPr1$x[,1:2])
dt_plants2 <- cbind(dt_plants_nounc2, myPr2$x[,1:2])
dt_plants5 <- cbind(dt_plants_nounc5, myPr5$x[,1:2])
dt_plants6 <- cbind(dt_plants_nounc6, myPr6$x[,1:2])
dt_plants15 <- cbind(dt_plants_nounce15, myPr15$x[,1:2])# Plot for all plot
myPr_all <- prcomp(dt_plants_nounc[,12:23], scale=TRUE)
dt_plants_all <- cbind(dt_plants_nounc, myPr_all$x[,1:2])
ggplot(dt_plants_all, aes(PC1, PC2, col=Plot, fill=Plot))+
stat_ellipse(geom="polygon", col="black", alpha=0.5)+
theme_classic()+
geom_point(shape=21, col="black")plot(myPr125, type="l")summary(myPr1)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7892 1.6134 1.2723 1.09515 1.00262 0.8604 0.71435
## Proportion of Variance 0.2668 0.2169 0.1349 0.09995 0.08377 0.0617 0.04252
## Cumulative Proportion 0.2668 0.4837 0.6186 0.71853 0.80230 0.8640 0.90652
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.63622 0.59865 0.4436 0.30908 0.25739
## Proportion of Variance 0.03373 0.02987 0.0164 0.00796 0.00552
## Cumulative Proportion 0.94025 0.97012 0.9865 0.99448 1.00000
library(readr)
library(dplyr)
library(tidyr)##
## Attaching package: 'tidyr'
## The following objects are masked from 'package:reshape':
##
## expand, smiths
## The following object is masked from 'package:reshape2':
##
## smiths
library(ropls)
dt_plants_nounc_3 <- dt_plants_nounc |> select(-Scientific_Name, -Group, -Plot, -Sample_Name, -Tube_No, -Type_of_Sample, -Cup_No, -pXRF_measurement_ID, -File, -Material)
typeof(dt_plants_nounc_3$Total_Weight)## [1] "character"
dt_plants_nounc_3[,1] <- sapply(dt_plants_nounc_3[,1],as.numeric)
dt_nounc_PCA <- opls(x=dt_plants_nounc_3)## PCA
## 226 samples x 13 variables
## standard scaling of predictors
## R2X(cum) pre ort
## Total 0.558 3 0
plot(dt_nounc_PCA)plot(dt_nounc_PCA, typeVc ="x-score", parAsColFcVn=dt_plants_nounc$Plot)dt_opls <-opls(dt_plants_nounc_3, dt_plants_nounc$Plot)## PLS-DA
## 226 samples x 13 variables and 1 response
## standard scaling of predictors and response(s)
## R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y pQ2
## Total 0.59 0.179 0.122 0.398 4 0 0.05 0.05
summary(dt_opls)## Length Class Mode
## 1 opls S4
##PCA
plot(dt_nounc_PCA, typeVc ="x-score", parAsColFcVn=dt_plants_nounc$Cu)dt_opls <-opls(dt_plants_nounc_3, dt_plants_nounc$Cu)## PCA
## 226 samples x 13 variables
## standard scaling of predictors
## R2X(cum) pre ort
## Total 0.558 3 0
dt_plants_trimmed <- dt_plants[c(-2,-4,-5,-6,-8,-10,-11, -24, -25, -40, -41, -42, -43, -44, -45, -seq(11,45,by=2))]
dt_plants_trimmed[,3] <- sapply(dt_plants_trimmed[,3],as.numeric)
library(vegan)## Loading required package: permute
## Loading required package: lattice
## Registered S3 methods overwritten by 'vegan':
## method from
## plot.rda klaR
## predict.rda klaR
## print.rda klaR
## This is vegan 2.6-4
# Create a matrix of the environmental variables (columns 5 to 18)
env_mat <- as.matrix(dt_plants_trimmed[,5:18])
# Create a data frame of the response variables (weight and thickness)
resp_df <- data.frame(weight = dt_plants_trimmed[,3], thickness = dt_plants_trimmed[,18])
# Perform RDA
rda_result <- rda(env_mat, resp_df)
# Print the RDA results
summary(rda_result)##
## Call:
## rda(X = env_mat, Y = resp_df)
##
## Partitioning of variance:
## Inertia Proportion
## Total 121406484 1.00000
## Constrained 1531857 0.01262
## Unconstrained 119874625 0.98738
##
## Eigenvalues, and their contribution to the variance
##
## Importance of components:
## RDA1 RDA2 PC1 PC2 PC3
## Eigenvalue 1.510e+06 2.150e+04 1.177e+08 2.056e+06 9.631e+04
## Proportion Explained 1.244e-02 1.771e-04 9.696e-01 1.694e-02 7.933e-04
## Cumulative Proportion 1.244e-02 1.262e-02 9.822e-01 9.992e-01 1.000e+00
## PC4 PC5 PC6 PC7 PC8
## Eigenvalue 3.911e+03 8.272e+02 3.950e+02 1.908e+02 6.841e+01
## Proportion Explained 3.221e-05 6.813e-06 3.253e-06 1.572e-06 5.635e-07
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
## PC9 PC10 PC11
## Eigenvalue 1.485e+01 1.433e+01 5.563e+00
## Proportion Explained 1.223e-07 1.180e-07 4.582e-08
## Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00
##
## Accumulated constrained eigenvalues
## Importance of components:
## RDA1 RDA2
## Eigenvalue 1.51e+06 2.150e+04
## Proportion Explained 9.86e-01 1.403e-02
## Cumulative Proportion 9.86e-01 1.000e+00
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 406.5426
##
##
## Species scores
##
## RDA1 RDA2 PC1 PC2 PC3
## Cl_concentration -1.118e+00 -2.6132416 3.913e+00 -5.290e+01 -3.458e-02
## Ca_concentration -4.527e+01 0.2982983 4.003e+02 5.182e-01 -7.806e-02
## Ti_concentration -1.996e-01 -0.8915015 4.554e-01 -3.743e-02 1.408e+00
## Cr_concentration 3.508e-03 -0.1006886 1.345e-02 -1.917e-02 5.380e-03
## Mn_concentration -4.304e-02 0.2687386 3.385e-01 5.274e-02 -1.114e-02
## Fe_concentration -2.259e+00 -4.6223101 2.690e+00 -1.426e-01 1.129e+01
## Ni_concentration 7.653e-04 0.0016839 4.491e-04 7.096e-04 7.266e-04
## Cu_concentration 1.159e-01 -0.2915491 2.933e-01 -9.958e-02 1.258e+00
## Zn_concentration 6.549e-02 0.1202517 4.283e-02 -1.816e-02 -2.305e-02
## As_concentration -9.716e-03 -0.0580838 9.539e-03 -5.080e-03 1.150e-02
## Se_concentration -3.236e-03 0.0208173 2.390e-02 -3.584e-03 3.048e-03
## Cd_concentration -1.077e-02 -0.0022981 7.833e-03 -1.386e-02 -5.672e-05
## Re_concentration -2.004e-03 0.0158494 5.890e-02 2.211e-03 -2.706e-02
## Substrate_RT 3.390e-04 0.0007463 -8.777e-20 -3.047e-20 -8.265e-20
## PC4
## Cl_concentration -3.409e-03
## Ca_concentration -3.582e-04
## Ti_concentration 4.713e-01
## Cr_concentration -7.629e-03
## Mn_concentration 3.109e-01
## Fe_concentration -3.046e-01
## Ni_concentration 3.964e-03
## Cu_concentration 2.213e+00
## Zn_concentration 1.224e-01
## As_concentration -4.268e-03
## Se_concentration 1.589e-02
## Cd_concentration -5.602e-04
## Re_concentration 3.057e-02
## Substrate_RT 5.996e-20
##
##
## Site scores (weighted sums of species scores)
##
## RDA1 RDA2 PC1 PC2 PC3 PC4
## sit1 -748.288 300.45656 86.041159 24.10835 48.47036 -38.5162
## sit2 -187.813 -40.15404 16.117741 26.87476 98.43987 -75.5024
## sit3 -727.041 322.94384 82.229273 26.35216 21.68294 -27.4388
## sit4 -524.078 -27.16805 62.738736 -15.11165 76.33183 -53.3936
## sit5 -331.634 -206.52794 35.002377 -56.45269 8.80916 -22.5313
## sit6 174.799 -33.40308 -25.770464 15.60207 -9.92309 -8.8928
## sit7 -650.223 143.24273 73.131123 3.41385 54.55562 -37.4839
## sit8 -386.750 96.39143 44.672582 25.25865 69.94003 -46.0739
## sit9 -745.184 497.69529 84.192780 37.02676 -47.60523 -7.9333
## sit10 -568.919 221.75330 63.664977 16.04563 18.87576 -4.0106
## sit11 -80.380 104.97444 3.526360 21.53936 -19.65551 1.5093
## sit12 -605.623 173.57852 66.536886 18.52929 52.72174 -42.6211
## sit13 -8.513 50.41842 3.435057 3.45504 -24.48045 -7.4393
## sit14 -148.519 185.93260 18.760273 18.37253 -21.00470 -7.2870
## sit15 -596.611 75.55122 70.295616 -38.93324 -34.89065 -14.3496
## sit16 79.231 -173.87110 -6.446185 -30.52783 -4.57959 15.0229
## sit17 -168.908 -52.48085 21.024237 -27.74468 0.20697 10.7570
## sit18 6.815 -310.59399 4.232789 -68.41588 -7.03167 5.9667
## sit19 -124.640 -326.82590 15.253766 -86.83163 -18.66540 -11.3164
## sit20 146.547 -245.73104 -15.937508 -42.37643 -26.34633 4.5219
## sit21 122.846 -132.78635 -10.834725 -18.25573 -11.18394 55.2803
## sit22 200.466 -64.34813 -18.069818 3.32289 4.75440 40.5778
## sit23 -615.596 271.16566 71.511922 2.46936 -5.17572 -12.4325
## sit24 259.177 0.85563 -29.577947 17.60335 -13.97539 -4.6871
## sit25 266.620 8.98485 -30.666277 20.92334 -9.31095 -7.2478
## sit26 258.435 -7.49668 -28.653557 17.94487 -8.53779 -8.4609
## sit27 153.246 -261.66388 -16.638184 -49.22149 -8.02597 -6.1885
## sit28 39.167 -53.33539 -6.107252 -16.73154 -6.69159 -4.5667
## sit29 150.806 -104.19684 -21.096727 -17.60074 -2.90268 2.2684
## sit30 124.056 -181.54589 -18.770932 -35.40456 -6.90481 0.8337
## sit31 99.008 -30.97522 -18.009435 -7.20354 -1.50738 4.1342
## sit32 76.988 -201.65480 -13.912810 -44.92209 -2.49988 7.6995
## sit33 33.707 152.78890 2.524112 27.78782 -8.83197 -15.0032
## sit34 154.415 100.16155 -16.685978 26.48838 -8.03291 -8.6156
## sit35 110.363 119.00874 -12.119048 27.35236 -12.48597 -5.6134
## sit36 -16.551 177.70702 1.354422 28.17276 -14.58806 -7.7783
## sit37 45.278 63.63532 -4.347834 18.72115 9.07470 -17.9615
## sit38 -49.401 131.62759 6.113839 28.52431 17.30283 -18.6096
## sit39 -24.380 87.24135 2.427142 19.73245 6.39496 -15.4812
## sit40 152.687 -248.14725 -14.741203 -45.53799 -10.15558 -14.3247
## sit41 249.785 -167.77662 -26.346705 -20.69576 -13.54343 -9.8384
## sit42 210.716 -331.60059 -20.130961 -57.99842 -8.90298 -15.7071
## sit43 245.374 -232.12551 -24.801573 -34.35524 -12.75188 -12.0942
## sit44 272.181 -7.43471 -31.403832 16.63648 -27.80567 -2.0533
## sit45 221.469 -147.03593 -22.182370 -19.30244 -3.69415 -13.6072
## sit46 264.622 -4.74984 -29.532091 15.37792 -11.60365 -3.8816
## sit47 258.371 -29.25715 -26.765124 12.05555 -11.32307 -9.2298
## sit48 212.701 -80.08197 -20.256920 -2.60538 4.17146 -10.1871
## sit49 224.537 -67.66907 -23.569622 -2.64693 -5.02341 -11.2431
## sit50 226.077 -54.12527 -20.259016 0.06004 0.34368 -16.2590
## sit51 -96.810 205.72062 12.250673 27.16387 3.38871 -7.9124
## sit52 -132.800 220.36496 10.831780 27.35963 1.21613 -5.3263
## sit53 -50.980 181.95707 -2.888454 25.35997 12.00751 -2.4280
## sit54 -142.930 224.85085 19.063073 29.57447 -12.61085 -4.4836
## sit55 -140.738 223.06683 9.242539 25.96804 12.56010 3.2297
## sit56 -51.756 182.11702 1.185210 26.75998 3.04667 -1.0673
## sit57 -234.242 150.40614 23.074373 25.35972 9.53387 51.1322
## sit58 -269.612 225.65484 33.483273 31.39909 9.33721 82.7049
## sit59 -158.872 157.08838 21.875580 28.94439 34.80909 62.7164
## sit60 -159.965 106.20688 20.575863 19.49614 11.83958 6.4980
## sit61 193.702 -170.86767 -19.505826 -26.16411 -5.81701 -10.8893
## sit62 206.494 -78.41755 -20.414612 -4.70982 0.26734 -10.2154
## sit63 206.024 -32.00430 -19.884322 5.42725 0.81945 -12.4216
## sit64 212.724 -182.92100 -20.696022 -27.45613 0.75272 -15.5415
## sit65 241.980 -158.57763 -24.969582 -19.81113 -2.20130 -10.0741
## sit66 246.615 -100.80957 -26.330919 -6.37332 -8.72407 -8.8134
## sit67 211.145 -178.23999 -22.005423 -26.91560 -7.24850 -11.6578
## sit68 191.902 -193.92546 -24.902517 -29.35027 -18.51768 -0.4056
## sit69 205.914 -137.29040 -21.843841 -17.25672 -3.85881 -9.0853
## sit70 217.233 -121.79368 -19.890729 -15.29298 -1.43271 -13.3713
## sit71 227.441 -127.28076 -21.159811 -14.88563 -3.44153 -13.9615
## sit72 167.869 -89.61862 -15.260086 -10.67650 0.40807 -8.2649
## sit73 255.065 -156.09183 -24.895549 -15.04885 -6.23792 3.7339
## sit74 240.603 -241.60657 -29.820213 -33.04971 -14.48961 11.3598
## sit75 187.265 -0.81771 -25.662064 13.87727 -21.84835 9.9688
## sit76 42.479 62.68688 -9.075301 6.55234 2.66782 -6.9099
## sit77 111.348 120.35204 -10.693135 25.80335 -0.06565 -11.6922
## sit78 8.398 165.92139 2.019080 26.14306 3.00624 -12.6842
## sit79 254.579 -79.59394 -34.615566 13.60594 5.80276 -13.7411
## sit80 245.156 -89.67004 -29.663812 10.15060 7.66462 -18.5437
## sit81 252.729 -117.32968 -33.206407 13.90277 28.98687 -14.2441
## sit82 255.212 -66.84026 -38.162957 21.25874 16.30463 -11.0192
## sit83 239.737 -81.73980 -26.026203 14.25518 27.86951 -28.7668
## sit84 253.566 -56.09576 -29.027590 8.82741 -18.19470 -7.0818
## sit85 262.550 -112.27131 -31.573637 8.12855 8.16954 -14.7728
## sit86 161.917 -119.65657 -18.029966 12.93695 61.87626 -14.8337
## sit87 254.209 -153.51181 -30.372792 -0.91158 17.05784 -23.9373
## sit88 242.813 -87.07001 -29.807868 8.76044 8.65178 -18.8549
## sit89 226.472 -218.74636 -31.997624 7.30947 72.30309 -52.9857
## sit90 20.703 101.51984 -1.332440 20.43590 -3.64103 -2.9770
## sit91 -82.389 -519.96531 7.300025 -117.54326 -6.43064 -10.9739
## sit92 55.422 127.54830 -6.940609 27.59761 -3.57134 -1.7760
## sit93 238.792 -10.46020 -27.024152 13.37932 2.03940 4.3624
## sit94 246.298 6.37027 -26.549628 15.13713 -5.35203 -8.0530
## sit95 238.625 -63.83978 -26.453954 0.19923 -3.35171 -8.2213
## sit96 245.749 -3.48213 -27.160029 14.29068 -12.16914 -6.9006
## sit97 201.290 -56.69459 -19.567087 -1.94614 -9.74758 -8.9149
## sit98 237.895 -110.90015 -26.744408 -9.54498 -5.42055 -9.8875
## sit99 245.540 -81.53818 -23.618211 -3.25286 -0.53172 -13.5478
## sit100 254.848 -35.89373 -25.465689 6.34791 5.48671 -12.8965
## sit101 214.273 -16.79464 -22.855333 6.25063 4.14047 -8.5796
## sit102 238.569 -4.04463 -23.223544 15.59292 1.03484 7.9953
## sit103 227.193 27.13234 -23.845995 20.87065 -5.78601 15.7749
## sit104 256.238 -24.82954 -27.221646 10.46128 -3.33351 -7.4014
## sit105 259.929 18.67681 -27.219621 19.35483 -2.47223 -9.5263
## sit106 264.234 -46.61973 -28.125225 6.08364 -12.41944 -7.8348
## sit107 -216.498 1.80233 24.074962 9.93871 48.03313 -35.8123
## sit108 91.988 -42.19449 -5.912381 3.68513 25.12218 -9.1438
## sit109 51.094 39.14833 -2.579173 13.30088 7.37183 -11.7534
## sit110 53.641 35.91342 -2.402636 11.18733 8.18624 -12.9741
## sit111 56.726 58.81808 -5.281617 20.07221 13.06854 -16.5787
## sit112 125.165 48.18890 -7.857260 15.04168 5.55472 -15.3621
## sit113 111.385 59.60788 -8.327878 17.66682 1.96552 -11.0302
## sit114 76.946 -2.37716 -8.084181 10.48797 36.57652 -2.2069
## sit115 -92.325 172.53034 8.511512 22.39173 -21.69025 -5.1730
## sit116 -15.117 167.60688 2.115569 28.56064 -12.34334 -9.7445
## sit117 165.616 90.91398 -20.225802 27.50555 -15.97260 -5.4895
## sit118 76.630 -85.25906 -6.637246 -15.11519 -1.02106 2.5869
## sit119 50.643 66.07586 -1.853287 11.27861 -2.26603 -3.7574
## sit120 85.444 -20.30047 -7.359657 -2.49753 -6.69355 -8.0563
## sit121 122.707 -98.19084 -12.221939 -14.99642 -15.89430 -0.8485
## sit122 84.227 -16.07175 -6.469645 -1.31703 0.55604 -8.6764
## sit123 108.977 0.03648 -9.800129 2.56670 -10.03996 -2.1925
## sit124 122.422 -148.45792 -10.885680 -27.55135 -0.33512 -6.8980
## sit125 121.144 -28.83753 -10.160265 -3.30702 -1.22049 -7.8079
## sit126 -82.445 -54.50279 7.670754 -22.03613 9.18441 -1.5559
## sit127 93.166 -157.04570 -7.991360 -14.53331 26.57292 -8.7169
## sit128 77.593 -28.01550 -8.587916 0.67307 16.22716 4.3074
## sit129 104.753 -258.59775 -10.850356 -41.37221 27.91371 -12.3642
## sit130 96.109 -207.14543 -9.782928 -27.85297 34.18142 -8.6709
## sit131 36.396 24.90795 -4.617700 0.78255 -15.08510 -8.1206
## sit132 -52.304 112.75630 2.592293 12.45947 -2.65620 -6.3511
## sit133 38.252 76.02191 -7.037579 10.74776 0.16535 -4.2906
## sit134 -50.408 54.81634 2.861190 -0.63958 3.59544 -2.5988
## sit135 67.996 73.39566 -9.258813 12.70002 12.57049 -3.9510
## sit136 -16.726 106.62242 3.492950 10.72180 11.71485 -8.9775
## sit137 -433.520 240.23359 47.089023 13.76842 -23.58467 7.0546
## sit138 -482.089 276.02866 52.746207 15.05392 -5.98468 6.0943
## sit139 -201.921 96.58420 21.005865 -2.08384 -26.57443 -5.0691
## sit140 -132.167 24.03040 11.101474 -10.31347 -32.31624 -2.5416
## sit141 -148.404 64.52020 14.912086 -4.80104 -21.38739 2.8086
## sit142 -516.542 311.34034 54.577673 16.56418 -18.03473 21.1904
## sit143 -202.392 66.37729 21.998787 -6.09403 -13.37921 6.2058
## sit144 -14.029 60.14635 1.625452 4.54566 -5.72596 1.8197
## sit145 -38.472 85.49532 2.588156 10.12348 -27.49221 7.7807
## sit146 -123.347 126.68213 13.823415 11.81992 -12.60819 2.1429
## sit147 -144.389 95.91495 14.232097 5.49700 4.73625 10.9238
## sit148 -10.954 82.17835 -3.015140 12.58333 -31.83507 12.4073
## sit149 -31.442 61.04834 6.515954 3.70180 -3.88126 13.6804
## sit150 -9.461 77.86515 6.594701 10.32944 -17.98949 -2.3436
## sit151 -66.417 137.05567 5.817779 17.18569 -18.97604 9.3211
## sit152 -45.717 185.38093 2.658559 27.99957 -9.73912 12.1573
## sit153 -145.550 172.93644 13.787954 15.39409 -9.28190 31.1128
## sit154 -266.423 137.59927 32.024667 -1.30952 -5.39993 21.0686
## sit155 -169.225 165.31598 18.986389 12.17421 -12.90751 20.0969
## sit156 -155.777 143.72281 18.691804 9.99572 -4.53641 45.7423
## sit157 -184.835 128.85486 19.861858 5.17628 -21.84975 37.0437
## sit158 17.739 -0.53960 0.014841 -5.93332 -4.95943 9.9670
## sit159 -46.025 127.81802 4.759508 17.10916 -5.09428 26.2970
## sit160 -224.803 233.09066 27.490741 23.19350 -9.51961 53.1410
## sit161 -79.089 -221.14105 6.679470 -50.73995 -18.33752 -7.0746
## sit162 -45.248 -369.96358 1.804031 -83.38880 -32.14035 -7.0966
## sit163 11.465 -69.91497 -5.047810 5.03074 16.59717 -31.2890
## sit164 -194.208 156.78833 22.705748 22.39116 11.41905 6.4365
## sit165 -852.882 310.33026 97.810182 -13.15595 -36.43033 -5.9120
## sit166 -839.543 98.29933 96.835422 -58.20712 -28.36290 -10.4112
## sit167 -774.209 240.13430 90.925602 -19.86314 -19.48343 -9.6463
## sit168 -99.081 215.43019 7.600086 29.16485 -20.68588 -1.3351
## sit169 -10.739 174.80713 0.009782 28.98806 -21.72113 -1.9512
## sit170 -371.775 340.18118 39.066487 31.84536 -30.02903 -1.5525
## sit171 -62.692 108.71662 4.068208 14.08929 -39.81954 10.0865
## sit172 32.146 52.74264 2.292207 16.92975 4.09174 -10.6937
## sit173 180.069 25.86089 -24.429569 22.01992 -27.03666 -0.5598
## sit174 304.539 33.68750 -39.619055 29.19337 -41.65585 6.9115
## sit175 205.110 18.01443 -23.390911 20.96546 -18.22905 -0.7580
## sit176 216.766 -36.42936 -25.582320 12.62906 -14.98924 10.3041
## sit177 96.517 17.89711 -6.503890 8.62349 7.63677 1.2157
## sit178 139.581 53.58411 -18.333495 19.98780 -32.10103 3.9865
## sit179 56.128 131.97119 -2.312555 27.46073 -3.27428 -4.5116
## sit180 173.284 -53.80066 -20.027045 0.76186 -18.49276 12.9132
## sit181 127.231 -100.40181 -16.622612 -3.39158 -7.88948 48.0986
## sit182 284.129 39.43733 -37.102009 29.28574 -39.68111 9.2589
## sit183 109.245 -569.51000 -17.146464 -66.20047 98.65932 105.2963
## sit184 111.358 -484.02872 -17.658369 -53.00357 86.13424 56.0367
## sit185 129.431 -360.43477 -16.694791 -48.24575 23.37100 39.7338
## sit186 118.838 -432.75452 -13.687153 4.21628 205.90425 68.4184
## sit187 99.048 -341.05228 -12.790794 -9.74765 118.86312 -21.2562
## sit188 -340.331 286.77348 38.465005 26.71037 -33.26481 22.5049
## sit189 -328.579 313.48043 41.205348 31.68503 -22.21377 20.8197
## sit190 -486.852 386.50857 53.471437 31.64695 -18.27292 21.9065
## sit191 1.986 8.86899 -0.716648 2.63793 -18.25159 -11.4839
## sit192 29.047 84.00206 -3.226319 21.87481 -5.57393 -13.4324
## sit193 -202.868 -659.07832 19.944600 -156.60820 -11.16897 -8.6405
## sit194 -17.621 -114.89330 2.904159 -20.30124 -4.10274 -20.8865
## sit195 -119.790 25.87020 11.473843 -1.92861 1.48166 -9.4709
## sit196 -222.003 -85.96149 21.743092 -32.69740 1.64625 -2.6726
## sit197 -173.736 -4.96110 19.476734 -15.38109 0.89773 -11.5852
## sit198 -305.814 14.42813 33.649070 -12.06702 21.64288 -15.4412
## sit199 120.270 -20.35288 -16.203925 7.91288 -11.57844 -7.0135
## sit200 -27.124 63.02206 1.226710 12.00154 -0.59756 -8.8178
## sit201 5.747 25.83490 -3.945567 7.45852 0.42666 -7.0758
## sit202 -114.651 -6.65658 11.873439 -12.63759 -10.19570 -11.7620
## sit203 123.662 -38.76633 -17.275638 8.04522 -12.76736 -7.3408
## sit204 54.306 30.37822 -7.467265 14.90368 -9.45420 -12.8983
## sit205 111.856 -331.80323 -17.648094 -65.74958 -18.14612 1.9109
## sit206 24.781 57.33488 -4.329275 22.09204 -1.71271 -8.7424
## sit207 -125.095 -146.31252 12.132166 -39.31498 -0.97049 1.8407
## sit208 -148.572 29.69314 15.191323 -6.64274 -4.08982 1.7061
## sit209 -286.008 43.50456 30.292833 -23.54810 -8.26241 12.7457
## sit210 -664.639 66.90110 67.927235 -53.58724 -13.49159 19.9997
## sit211 -1.275 -100.45951 -0.155144 -10.64242 5.36228 -20.0842
## sit212 -2.049 -67.26187 3.639454 -16.35456 4.90268 5.2059
## sit213 104.498 -90.24505 -11.660085 -13.67643 -21.72238 13.6856
## sit214 -3.448 11.14001 5.014643 -1.24465 1.22981 -6.9281
## sit215 33.437 15.92728 -0.875059 2.78651 0.86674 17.4551
## sit216 49.682 -58.90628 -3.856863 -9.79648 -10.87969 20.9930
## sit217 55.522 4.38652 -5.814775 -0.95951 -11.04774 -4.9160
## sit218 -38.059 50.75543 4.097134 9.07128 -6.30040 17.5248
## sit219 23.831 3.85582 2.943887 1.48965 5.26501 37.0660
## sit220 39.686 10.00863 -5.029459 0.72998 -11.09028 13.9031
## sit221 -24.374 25.80965 8.669958 11.44561 29.93242 208.1649
## sit222 28.567 48.86093 -5.432714 16.57377 -2.17726 163.0368
## sit223 9.200 0.14379 -3.906001 -4.69072 -9.96570 11.4926
## sit224 23.020 51.43123 -4.507207 5.15230 -10.54634 -7.8491
## sit225 205.962 -3.21608 -20.305432 7.31003 13.41509 -16.0238
## sit226 126.286 108.51427 -9.182775 25.41918 6.86929 -18.1483
##
##
## Site constraints (linear combinations of constraining variables)
##
## RDA1 RDA2 PC1 PC2 PC3 PC4
## con1 10.6276 -7.7079 86.041159 24.10835 48.47036 -38.5162
## con2 -45.0506 -40.3300 16.117741 26.87476 98.43987 -75.5024
## con3 -2.0746 -32.7878 82.229273 26.35216 21.68294 -27.4388
## con4 30.3252 -21.1917 62.738736 -15.11165 76.33183 -53.3936
## con5 -21.8543 -36.8509 35.002377 -56.45269 8.80916 -22.5313
## con6 -52.8478 -41.2367 -25.770464 15.60207 -9.92309 -8.8928
## con7 -4.7349 -12.9640 73.131123 3.41385 54.55562 -37.4839
## con8 7.5466 -41.3908 44.672582 25.25865 69.94003 -46.0739
## con9 -3.8419 -42.6501 84.192780 37.02676 -47.60523 -7.9333
## con10 -7.5320 -29.0801 63.664977 16.04563 18.87576 -4.0106
## con11 -49.8698 -45.2584 3.526360 21.53936 -19.65551 1.5093
## con12 -18.5720 -35.8922 66.536886 18.52929 52.72174 -42.6211
## con13 21.4605 -21.9911 3.435057 3.45504 -24.48045 -7.4393
## con14 16.3842 -1.2022 18.760273 18.37253 -21.00470 -7.2870
## con15 23.6414 -34.2240 70.295616 -38.93324 -34.89065 -14.3496
## con16 22.8607 -15.5595 -6.446185 -30.52783 -4.57959 15.0229
## con17 16.9882 11.0919 21.024237 -27.74468 0.20697 10.7570
## con18 45.2643 -12.0167 4.232789 -68.41588 -7.03167 5.9667
## con19 11.2084 2.1087 15.253766 -86.83163 -18.66540 -11.3164
## con20 6.4511 -39.7303 -15.937508 -42.37643 -26.34633 4.5219
## con21 27.4698 -30.6658 -10.834725 -18.25573 -11.18394 55.2803
## con22 41.0835 -12.1442 -18.069818 3.32289 4.75440 40.5778
## con23 14.9744 14.7839 71.511922 2.46936 -5.17572 -12.4325
## con24 -2.1292 -11.1524 -29.577947 17.60335 -13.97539 -4.6871
## con25 -4.2909 -6.4140 -30.666277 20.92334 -9.31095 -7.2478
## con26 5.3361 -13.8142 -28.653557 17.94487 -8.53779 -8.4609
## con27 7.2918 12.2618 -16.638184 -49.22149 -8.02597 -6.1885
## con28 -14.4701 34.3125 -6.107252 -16.73154 -6.69159 -4.5667
## con29 -34.9707 43.5450 -21.096727 -17.60074 -2.90268 2.2684
## con30 -40.9395 35.8162 -18.770932 -35.40456 -6.90481 0.8337
## con31 -59.7101 59.8579 -18.009435 -7.20354 -1.50738 4.1342
## con32 -44.9519 52.6077 -13.912810 -44.92209 -2.49988 7.6995
## con33 55.3919 -3.7384 2.524112 27.78782 -8.83197 -15.0032
## con34 6.7137 14.6096 -16.685978 26.48838 -8.03291 -8.6156
## con35 2.8745 5.6754 -12.119048 27.35236 -12.48597 -5.6134
## con36 -5.2535 12.7277 1.354422 28.17276 -14.58806 -7.7783
## con37 6.7116 6.0521 -4.347834 18.72115 9.07470 -17.9615
## con38 4.2111 8.2819 6.113839 28.52431 17.30283 -18.6096
## con39 -3.2458 -2.2803 2.427142 19.73245 6.39496 -15.4812
## con40 23.3781 -1.6473 -14.741203 -45.53799 -10.15558 -14.3247
## con41 17.6541 -7.4425 -26.346705 -20.69576 -13.54343 -9.8384
## con42 34.1069 -5.8276 -20.130961 -57.99842 -8.90298 -15.7071
## con43 27.1177 -10.4740 -24.801573 -34.35524 -12.75188 -12.0942
## con44 -5.3595 -34.4539 -31.403832 16.63648 -27.80567 -2.0533
## con45 26.1459 11.2128 -22.182370 -19.30244 -3.69415 -13.6072
## con46 3.7853 -1.8731 -29.532091 15.37792 -11.60365 -3.8816
## con47 21.9998 -19.3922 -26.765124 12.05555 -11.32307 -9.2298
## con48 34.1501 6.6236 -20.256920 -2.60538 4.17146 -10.1871
## con49 16.6731 13.5362 -23.569622 -2.64693 -5.02341 -11.2431
## con50 47.4213 13.2304 -20.259016 0.06004 0.34368 -16.2590
## con51 10.7940 42.3793 12.250673 27.16387 3.38871 -7.9124
## con52 -37.7393 57.0639 10.831780 27.35963 1.21613 -5.3263
## con53 -76.7693 93.5442 -2.888454 25.35997 12.00751 -2.4280
## con54 24.5370 -1.8871 19.063073 29.57447 -12.61085 -4.4836
## con55 -59.5483 92.0520 9.242539 25.96804 12.56010 3.2297
## con56 -41.7389 56.4592 1.185210 26.75998 3.04667 -1.0673
## con57 -31.1120 -29.4852 23.074373 25.35972 9.53387 51.1322
## con58 25.1934 -17.9472 33.483273 31.39909 9.33721 82.7049
## con59 33.8900 10.3464 21.875580 28.94439 34.80909 62.7164
## con60 21.2772 -32.1930 20.575863 19.49614 11.83958 6.4980
## con61 22.0787 7.4190 -19.505826 -26.16411 -5.81701 -10.8893
## con62 26.5463 12.1008 -20.414612 -4.70982 0.26734 -10.2154
## con63 30.5821 9.5266 -19.884322 5.42725 0.81945 -12.4216
## con64 30.7006 17.3176 -20.696022 -27.45613 0.75272 -15.5415
## con65 22.0989 14.0688 -24.969582 -19.81113 -2.20130 -10.0741
## con66 14.4234 0.5148 -26.330919 -6.37332 -8.72407 -8.8134
## con67 17.4766 9.0182 -22.005423 -26.91560 -7.24850 -11.6578
## con68 -27.3982 -6.2776 -24.902517 -29.35027 -18.51768 -0.4056
## con69 13.5366 9.7630 -21.843841 -17.25672 -3.85881 -9.0853
## con70 42.0740 14.2614 -19.890729 -15.29298 -1.43271 -13.3713
## con71 41.0614 6.8777 -21.159811 -14.88563 -3.44153 -13.9615
## con72 33.4855 12.1454 -15.260086 -10.67650 0.40807 -8.2649
## con73 35.7050 -14.1573 -24.895549 -15.04885 -6.23792 3.7339
## con74 -21.9623 -13.4527 -29.820213 -33.04971 -14.48961 11.3598
## con75 -39.5310 -22.8150 -25.662064 13.87727 -21.84835 9.9688
## con76 -37.6406 66.9039 -9.075301 6.55234 2.66782 -6.9099
## con77 16.5961 33.1185 -10.693135 25.80335 -0.06565 -11.6922
## con78 25.7832 41.3046 2.019080 26.14306 3.00624 -12.6842
## con79 -50.8672 -19.3308 -34.615566 13.60594 5.80276 -13.7411
## con80 -16.5377 -25.9330 -29.663812 10.15060 7.66462 -18.5437
## con81 -40.0456 -20.8854 -33.206407 13.90277 28.98687 -14.2441
## con82 -81.5423 -12.3700 -38.162957 21.25874 16.30463 -11.0192
## con83 10.2726 -11.9860 -26.026203 14.25518 27.86951 -28.7668
## con84 -2.7763 -35.8977 -29.027590 8.82741 -18.19470 -7.0818
## con85 -15.9481 -31.7450 -31.573637 8.12855 8.16954 -14.7728
## con86 3.3543 -7.5731 -18.029966 12.93695 61.87626 -14.8337
## con87 -13.4407 -17.6375 -30.372792 -0.91158 17.05784 -23.9373
## con88 -20.1168 -14.4738 -29.807868 8.76044 8.65178 -18.8549
## con89 -55.0452 -14.7904 -31.997624 7.30947 72.30309 -52.9857
## con90 8.5570 1.6363 -1.332440 20.43590 -3.64103 -2.9770
## con91 -16.0151 2.7605 7.300025 -117.54326 -6.43064 -10.9739
## con92 -6.3084 12.2706 -6.940609 27.59761 -3.57134 -1.7760
## con93 0.2506 18.7842 -27.024152 13.37932 2.03940 4.3624
## con94 11.8363 12.3542 -26.549628 15.13713 -5.35203 -8.0530
## con95 5.2905 16.5208 -26.453954 0.19923 -3.35171 -8.2213
## con96 5.8455 -4.3583 -27.160029 14.29068 -12.16914 -6.9006
## con97 28.6603 -0.4923 -19.567087 -1.94614 -9.74758 -8.9149
## con98 2.1474 12.6762 -26.744408 -9.54498 -5.42055 -9.8875
## con99 37.3065 11.1060 -23.618211 -3.25286 -0.53172 -13.5478
## con100 30.2180 28.1613 -25.465689 6.34791 5.48671 -12.8965
## con101 12.6488 36.9743 -22.855333 6.25063 4.14047 -8.5796
## con102 33.4939 0.2650 -23.223544 15.59292 1.03484 7.9953
## con103 16.4599 -4.0944 -23.845995 20.87065 -5.78601 15.7749
## con104 15.9534 8.9394 -27.221646 10.46128 -3.33351 -7.4014
## con105 19.5167 12.1887 -27.219621 19.35483 -2.47223 -9.5263
## con106 15.9607 -5.8407 -28.125225 6.08364 -12.41944 -7.8348
## con107 -3.8223 -36.2832 24.074962 9.93871 48.03313 -35.8123
## con108 40.0554 5.8596 -5.912381 3.68513 25.12218 -9.1438
## con109 28.1997 -2.0934 -2.579173 13.30088 7.37183 -11.7534
## con110 32.3494 5.8811 -2.402636 11.18733 8.18624 -12.9741
## con111 9.9421 4.9547 -5.281617 20.07221 13.06854 -16.5787
## con112 55.6730 13.0502 -7.857260 15.04168 5.55472 -15.3621
## con113 37.6573 6.7557 -8.327878 17.66682 1.96552 -11.0302
## con114 5.8621 41.6123 -8.084181 10.48797 36.57652 -2.2069
## con115 -17.8845 -1.7370 8.511512 22.39173 -21.69025 -5.1730
## con116 2.9111 2.4966 2.115569 28.56064 -12.34334 -9.7445
## con117 -13.4068 -2.5705 -20.225802 27.50555 -15.97260 -5.4895
## con118 18.3482 6.2042 -6.637246 -15.11519 -1.02106 2.5869
## con119 34.0780 14.1838 -1.853287 11.27861 -2.26603 -3.7574
## con120 20.5124 3.3048 -7.359657 -2.49753 -6.69355 -8.0563
## con121 15.0136 -16.3297 -12.221939 -14.99642 -15.89430 -0.8485
## con122 27.2040 12.2361 -6.469645 -1.31703 0.55604 -8.6764
## con123 22.3975 1.2268 -9.800129 2.56670 -10.03996 -2.1925
## con124 26.9032 17.5814 -10.885680 -27.55135 -0.33512 -6.8980
## con125 31.5885 17.9705 -10.160265 -3.30702 -1.22049 -7.8079
## con126 -14.3093 41.5419 7.670754 -22.03613 9.18441 -1.5559
## con127 23.2344 -13.4719 -7.991360 -14.53331 26.57292 -8.7169
## con128 2.0194 25.6764 -8.587916 0.67307 16.22716 4.3074
## con129 10.0929 24.9130 -10.850356 -41.37221 27.91371 -12.3642
## con130 10.6934 19.5971 -9.782928 -27.85297 34.18142 -8.6709
## con131 -4.5001 8.8413 -4.617700 0.78255 -15.08510 -8.1206
## con132 -29.6879 40.6689 2.592293 12.45947 -2.65620 -6.3511
## con133 -23.9981 48.8653 -7.037579 10.74776 0.16535 -4.2906
## con134 -25.1239 55.1197 2.861190 -0.63958 3.59544 -2.5988
## con135 -13.7434 66.9157 -9.258813 12.70002 12.57049 -3.9510
## con136 14.0214 65.6098 3.492950 10.72180 11.71485 -8.9775
## con137 -18.7370 -23.1078 47.089023 13.76842 -23.58467 7.0546
## con138 -17.2474 19.3298 52.746207 15.05392 -5.98468 6.0943
## con139 -16.9150 -12.1086 21.005865 -2.08384 -26.57443 -5.0691
## con140 -34.4262 -23.4455 11.101474 -10.31347 -32.31624 -2.5416
## con141 -17.0357 -2.9267 14.912086 -4.80104 -21.38739 2.8086
## con142 -35.7113 20.2049 54.577673 16.56418 -18.03473 21.1904
## con143 -8.4219 -1.3740 21.998787 -6.09403 -13.37921 6.2058
## con144 0.1651 22.1705 1.625452 4.54566 -5.72596 1.8197
## con145 -16.1213 -22.3560 2.588156 10.12348 -27.49221 7.7807
## con146 -1.7807 1.6117 13.823415 11.81992 -12.60819 2.1429
## con147 -18.9251 31.3292 14.232097 5.49700 4.73625 10.9238
## con148 -38.1120 -26.7069 -3.015140 12.58333 -31.83507 12.4073
## con149 25.9113 14.0894 6.515954 3.70180 -3.88126 13.6804
## con150 48.3271 -26.7798 6.594701 10.32944 -17.98949 -2.3436
## con151 -15.6181 1.0314 5.817779 17.18569 -18.97604 9.3211
## con152 -22.8709 25.6378 2.658559 27.99957 -9.73912 12.1573
## con153 -24.3356 36.7388 13.787954 15.39409 -9.28190 31.1128
## con154 15.9602 27.8934 32.024667 -1.30952 -5.39993 21.0686
## con155 -2.1435 20.6709 18.986389 12.17421 -12.90751 20.0969
## con156 8.8244 25.0714 18.691804 9.99572 -4.53641 45.7423
## con157 -10.0126 -2.0251 19.861858 5.17628 -21.84975 37.0437
## con158 17.9168 18.3966 0.014841 -5.93332 -4.95943 9.9670
## con159 -4.4157 20.8722 4.759508 17.10916 -5.09428 26.2970
## con160 17.1089 14.0033 27.490741 23.19350 -9.51961 53.1410
## con161 -19.4888 -35.6286 6.679470 -50.73995 -18.33752 -7.0746
## con162 -28.2199 -38.5365 1.804031 -83.38880 -32.14035 -7.0966
## con163 -32.9483 -46.4971 -5.047810 5.03074 16.59717 -31.2890
## con164 5.7627 -3.6592 22.705748 22.39116 11.41905 6.4365
## con165 9.5500 -14.8150 97.810182 -13.15595 -36.43033 -5.9120
## con166 15.1722 4.3365 96.835422 -58.20712 -28.36290 -10.4112
## con167 27.8100 -0.2929 90.925602 -19.86314 -19.48343 -9.6463
## con168 -32.7884 13.8440 7.600086 29.16485 -20.68588 -1.3351
## con169 -11.3937 -3.0801 0.009782 28.98806 -21.72113 -1.9512
## con170 -28.1264 4.8249 39.066487 31.84536 -30.02903 -1.5525
## con171 -27.4944 -43.7694 4.068208 14.08929 -39.81954 10.0865
## con172 52.1122 -27.1862 2.292207 16.92975 4.09174 -10.6937
## con173 -36.0523 -47.9333 -24.429569 22.01992 -27.03666 -0.5598
## con174 -45.8240 -51.5799 -39.619055 29.19337 -41.65585 6.9115
## con175 -1.7386 -37.9174 -23.390911 20.96546 -18.22905 -0.7580
## con176 -9.2322 -39.5724 -25.582320 12.62906 -14.98924 10.3041
## con177 39.0881 12.7660 -6.503890 8.62349 7.63677 1.2157
## con178 -22.7995 -39.6311 -18.333495 19.98780 -32.10103 3.9865
## con179 35.2184 4.1825 -2.312555 27.46073 -3.27428 -4.5116
## con180 -3.5534 -25.2950 -20.027045 0.76186 -18.49276 12.9132
## con181 -19.4104 -44.3196 -16.622612 -3.39158 -7.88948 48.0986
## con182 -44.0183 -50.4832 -37.102009 29.28574 -39.68111 9.2589
## con183 -39.7964 -18.8100 -17.146464 -66.20047 98.65932 105.2963
## con184 -42.5427 -16.3214 -17.658369 -53.00357 86.13424 56.0367
## con185 -16.7225 -33.3769 -16.694791 -48.24575 23.37100 39.7338
## con186 0.2383 -29.9440 -13.687153 4.21628 205.90425 68.4184
## con187 -12.2987 -33.1548 -12.790794 -9.74765 118.86312 -21.2562
## con188 -1.9391 -29.1677 38.465005 26.71037 -33.26481 22.5049
## con189 34.0137 -15.3841 41.205348 31.68503 -22.21377 20.8197
## con190 -16.0426 25.1058 53.471437 31.64695 -18.27292 21.9065
## con191 -4.5715 -34.6594 -0.716648 2.63793 -18.25159 -11.4839
## con192 0.1561 -19.0610 -3.226319 21.87481 -5.57393 -13.4324
## con193 -24.3495 -0.4621 19.944600 -156.60820 -11.16897 -8.6405
## con194 8.3111 -35.0923 2.904159 -20.30124 -4.10274 -20.8865
## con195 -18.5482 0.2488 11.473843 -1.92861 1.48166 -9.4709
## con196 -29.6588 1.1384 21.743092 -32.69740 1.64625 -2.6726
## con197 -1.6866 6.4300 19.476734 -15.38109 0.89773 -11.5852
## con198 -8.6106 1.1535 33.649070 -12.06702 21.64288 -15.4412
## con199 -22.8930 -25.6374 -16.203925 7.91288 -11.57844 -7.0135
## con200 -16.5187 1.2967 1.226710 12.00154 -0.59756 -8.8178
## con201 -29.1727 4.4393 -3.945567 7.45852 0.42666 -7.0758
## con202 -9.8226 -4.0574 11.873439 -12.63759 -10.19570 -11.7620
## con203 -28.9677 -42.9946 -17.275638 8.04522 -12.76736 -7.3408
## con204 -11.9048 -32.9040 -7.467265 14.90368 -9.45420 -12.8983
## con205 -42.8276 5.2914 -17.648094 -65.74958 -18.14612 1.9109
## con206 -13.8004 -36.4140 -4.329275 22.09204 -1.71271 -8.7424
## con207 -17.4239 -1.1967 12.132166 -39.31498 -0.97049 1.8407
## con208 -14.5278 3.5430 15.191323 -6.64274 -4.08982 1.7061
## con209 -18.5363 40.2783 30.292833 -23.54810 -8.26241 12.7457
## con210 -64.8027 73.2901 67.927235 -53.58724 -13.49159 19.9997
## con211 -2.3909 -39.0423 -0.155144 -10.64242 5.36228 -20.0842
## con212 30.3841 7.3855 3.639454 -16.35456 4.90268 5.2059
## con213 1.6674 -27.2578 -11.660085 -13.67643 -21.72238 13.6856
## con214 40.8130 2.6100 5.014643 -1.24465 1.22981 -6.9281
## con215 25.6738 6.2867 -0.875059 2.78651 0.86674 17.4551
## con216 15.7135 -19.9385 -3.856863 -9.79648 -10.87969 20.9930
## con217 4.1408 7.5678 -5.814775 -0.95951 -11.04774 -4.9160
## con218 -2.1590 -18.0860 4.097134 9.07128 -6.30040 17.5248
## con219 49.8083 -3.9328 2.943887 1.48965 5.26501 37.0660
## con220 -4.8069 3.2242 -5.029459 0.72998 -11.09028 13.9031
## con221 52.1201 -5.9690 8.669958 11.44561 29.93242 208.1649
## con222 -19.7263 -18.5373 -5.432714 16.57377 -2.17726 163.0368
## con223 -25.2783 17.7636 -3.906001 -4.69072 -9.96570 11.4926
## con224 -16.9301 22.6142 -4.507207 5.15230 -10.54634 -7.8491
## con225 26.9094 54.0742 -20.305432 7.31003 13.41509 -16.0238
## con226 44.9391 30.9500 -9.182775 25.41918 6.86929 -18.1483
##
##
## Biplot scores for constraining variables
##
## RDA1 RDA2 PC1 PC2 PC3 PC4
## weight -0.09769 0.9952 0 0 0 0
## thickness 0.41362 0.9104 0 0 0 0
#Plot
# Plot the RDA biplot
plot(rda_result, scaling = 3)
# Add labels to the plot
text(rda_result, display = "species", col = "blue", cex = 0.8) #species - environmental variales
text(rda_result, display = "sites", col = "red", cex = 0.8) # the sites - response variables
# Add a title to the plot
title(main = "Redundancy Analysis (RDA) Biplot")